We design and build real-world systems to understand how AI, DataOps, and industrial data can be combined to solve complex manufacturing problems — from downtime and bottlenecks to performance and optimisation.
This is not theory. It is applied engineering, experimentation, and learning in real industrial contexts.
Industrial AI is not a solved problem. These are the areas where we are doing active engineering research — building systems, testing hypotheses, and forming grounded positions on what the technology can and cannot do today.
How do you structure OT, MES, and SAP data properly so that AI can reason over it meaningfully? The data architecture problem is harder — and more consequential — than the AI problem.
How can multi-agent systems support industrial decision-making in a way that operations teams will actually trust and use? What does reliable agentic reasoning over live operational data look like in practice?
Why are ISA-95 context modelling and digital twins critical for AI — not just for visualisation? How does adding operational context change what AI can reason about and how reliably it does so?
What does it take to move from dashboards to real decision support? How do you design AI systems that fit into how industrial people actually work — under time pressure, with incomplete information?
Every experiment follows the same engineering discipline — connecting real systems, structuring data properly, adding operational context, then applying and stress-testing AI.
Integrate OT systems, ERP, historian, and operational data sources into a single governed data estate. Real data, not synthetic — the messiness is part of the experiment.
Organise raw data using industrial standards — ISA-95 hierarchies, equipment context, consistent data models. This is where most industrial AI projects fail, and where we spend the most time.
Add the operational meaning that makes data useful to an AI agent — production orders, shift patterns, equipment relationships, process limits, and what normal looks like.
Deploy AI agents against the structured, contextualised data. Test what they can reason over reliably, where they fail, and what the failure modes reveal about the underlying design.
Measure whether the system produces outputs that operations teams can act on — and publish what we find, including the failures and the open questions.
Document findings, refine the architecture, and push the experiment further. The goal is not a finished product — it is a clearer picture of what industrial AI engineering actually requires.
These are live experiments — not products, not demos, not marketing assets. Each one is built to test a specific hypothesis about what industrial AI can do, running on real infrastructure with real data against real engineering constraints.
A live exploration of how AI can be applied to industrial operations. This prototype demonstrates how real-time manufacturing data, AI agents, and contextual models can be combined to analyse performance, identify issues, and support operational decision-making.
Central hypothesis: Can a tiered multi-agent architecture — where Tier 1 agents analyse, Tier 2 agents diagnose root causes, and Tier 3 agents orchestrate and synthesise — reason meaningfully over live manufacturing data without a data analyst in the loop? Built on Microsoft Fabric, Claude Sonnet 4.6, and a NovaChem chemical plant scenario using real operational data patterns.
A live exploration of industrial data standards as a foundation for AI. Built on the CESMII i3x address space and ISA-95 hierarchy, this prototype tests whether structuring OT and SAP data to open industrial standards creates a more reliable and meaningful context for AI reasoning.
Central hypothesis: Standards-based context modelling — ISA-95 equipment hierarchies, VQT data models, OPC UA compatibility — produces a richer and more durable AI reasoning surface than ad-hoc data structures. The explorer lets you navigate the full industrial knowledge graph: plant → line → equipment → OT signals → production orders.
A platform built to explore one of the most important and least-discussed questions in industrial AI: how do you design AI systems that can actually be trusted in operational environments? Not trusted because they are accurate — trusted because every decision is explainable, auditable, and governed.
Central hypothesis: Deploying AI agents in industrial operations requires a governance architecture that is as carefully engineered as the agents themselves — covering the full lifecycle from build and test through to deployment oversight and continuous control. This platform demonstrates what that architecture looks like in practice: audit trails, confidence thresholds, guardrails, human oversight patterns, agent performance benchmarking, and compliance frameworks for regulated environments.
An experiment in autonomous industrial data modelling. We are testing whether AI can take raw, unstructured data in a Microsoft Fabric Lakehouse and autonomously generate a production-grade semantic model — ISA-95 equipment hierarchies, entity relationships, DAX measures, and ontological structure — with minimal human input.
Central hypothesis: The most expensive part of industrial data projects is not the infrastructure — it is the modelling. Can AI compress weeks of data engineering work into hours? And if it can, what does it get wrong, and why?
The failure point is almost always the data foundation. Raw OT and SAP data is not AI-ready. Without structured, contextualised, governed data, AI has nothing meaningful to reason over. Fix the data architecture first — every time.
Data without operational meaning produces AI outputs that operations teams cannot trust or act on. Equipment hierarchies, process limits, production schedules, shift context — this is not metadata. It is the difference between AI that reasons and AI that guesses.
The industrial analytics market has spent twenty years building better dashboards. The question that actually matters is: what decision does this enable, and does the person making it have what they need to act? Agentic AI changes this — but only when it is designed around operational reality, not technology capability.
Static predictive models require constant retraining, domain expertise to build, and rarely survive contact with real operational variability. Agentic systems that reason over live data, call tools, and explain their outputs are a fundamentally different and more durable architecture for industrial intelligence.
What does it actually take — in data architecture, agent design, and operational integration — to make AI genuinely useful in an industrial environment? We are engineering the answer.
Every prototype follows the same discipline. Every prototype surfaces something real.
We start with the industrial problem — not the AI capability. What decision needs to be made? What data exists? What does "good" look like in this environment?
We design the solution before we build it — data model, agent architecture, trust patterns, interoperability approach. Design decisions here determine whether AI is useful or not.
Real infrastructure. Real data pipelines. Real AI models. We build on Microsoft Fabric and Azure so findings reflect production constraints, not sandbox conditions.
Each prototype is a working application — not a demo. We run it against live data, stress-test the AI reasoning, and surface what breaks, what surprises, and where the value actually is.
Agentic AI in manufacturing only works when the agent has a well-defined toolset, structured operational context, and clear boundaries. Open-ended reasoning over raw industrial data produces plausible-sounding answers that operations teams cannot act on.
The limiting factor for AI in manufacturing is not the model — it is the data layer. SCADA, ERP, MES and historians use incompatible schemas and semantics. Until that is resolved, AI reasoning across systems produces noise, not insight.
Operations teams reject AI that is right but unexplained. Confidence signals, audit trails, and human override are not nice-to-haves — they are the difference between adoption and abandonment. The application design matters as much as the model.
Raw tables give AI data. Semantic models give AI understanding. When industrial data is structured with ISA-95 context, entity relationships, and ontological hierarchy, the quality of AI-generated insight improves dramatically — and becomes auditable.
Amplify Industrial is a research and engineering practice focused on a single question: what can AI actually do in industrial environments, and what does it take to make it work reliably?
We sit at the intersection of deep industrial operations knowledge and frontier AI engineering — building live systems, running real experiments, and forming grounded positions on what the technology can and cannot do today.
We are not a vendor, a system integrator, or a product company. We explore, design, and define how AI should be applied in industry — through hands-on engineering, real infrastructure, and published findings.
Our work spans agentic AI system design, industrial data architecture, semantic modelling, and the human factors of deploying AI in operational environments. We work with Microsoft Fabric, Azure, and Claude — not because they are fashionable, but because they are currently the most capable tools for this specific problem space.
Engineering the frontier. Grounded in operations.
If you're exploring industrial AI, working on similar problems, or want to discuss the research — we'd be glad to hear from you. Leave your details and we'll be in touch.